import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_pytesseract_available():
     from PIL import Image

     from transformers import LayoutLMvaImageProcessor
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def __init__(     self						:						Any   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						str=7   ,   lowerCamelCase_						:						Tuple=3   ,   lowerCamelCase_						:						List[Any]=18   ,   lowerCamelCase_						:						Dict=30   ,   lowerCamelCase_						:						List[Any]=400   ,   lowerCamelCase_						:						Optional[Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      size if size is not None else {"""height""": 18, """width""": 18}
    UpperCamelCase			  =      parent
    UpperCamelCase			  =      batch_size
    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      image_size
    UpperCamelCase			  =      min_resolution
    UpperCamelCase			  =      max_resolution
    UpperCamelCase			  =      do_resize
    UpperCamelCase			  =      size
    UpperCamelCase			  =      apply_ocr







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				LayoutLMvaImageProcessor if is_pytesseract_available() else None







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      LayoutLMvaImageProcessingTester(self				)







   @property
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    return self.image_processor_tester.prepare_image_processor_dict()







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """do_resize"""				)				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """size"""				)				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """apply_ocr"""				)				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class.from_dict(self.image_processor_dict				)
    self.assertEqual(image_processor.size   ,   {"""height""": 18, """width""": 18}				)

    UpperCamelCase			  =      self.image_processing_class.from_dict(self.image_processor_dict   ,   size=42				)
    self.assertEqual(image_processor.size   ,   {"""height""": 42, """width""": 42}				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    pass







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    # create random PIL images
    UpperCamelCase			  =      prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=lowerCamelCase_				)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_   ,   Image.Image				)

    # Test not batched input
    UpperCamelCase			  =      image_processing(image_inputs[0]   ,   return_tensors="""pt"""				)
    self.assertEqual(
        encoding.pixel_values.shape   ,   (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )

    self.assertIsInstance(encoding.words   ,   lowerCamelCase_				)
    self.assertIsInstance(encoding.boxes   ,   lowerCamelCase_				)

    # Test batched
    UpperCamelCase			  =      image_processing(lowerCamelCase_   ,   return_tensors="""pt"""				).pixel_values
    self.assertEqual(
        encoded_images.shape   ,   (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    # create random numpy tensors
    UpperCamelCase			  =      prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=lowerCamelCase_   ,   numpify=lowerCamelCase_				)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_   ,   np.ndarray				)

    # Test not batched input
    UpperCamelCase			  =      image_processing(image_inputs[0]   ,   return_tensors="""pt"""				).pixel_values
    self.assertEqual(
        encoded_images.shape   ,   (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )

    # Test batched
    UpperCamelCase			  =      image_processing(lowerCamelCase_   ,   return_tensors="""pt"""				).pixel_values
    self.assertEqual(
        encoded_images.shape   ,   (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    # create random PyTorch tensors
    UpperCamelCase			  =      prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=lowerCamelCase_   ,   torchify=lowerCamelCase_				)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_   ,   torch.Tensor				)

    # Test not batched input
    UpperCamelCase			  =      image_processing(image_inputs[0]   ,   return_tensors="""pt"""				).pixel_values
    self.assertEqual(
        encoded_images.shape   ,   (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )

    # Test batched
    UpperCamelCase			  =      image_processing(lowerCamelCase_   ,   return_tensors="""pt"""				).pixel_values
    self.assertEqual(
        encoded_images.shape   ,   (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      LayoutLMvaImageProcessor()

    from datasets import load_dataset

    UpperCamelCase			  =      load_dataset("""hf-internal-testing/fixtures_docvqa"""   ,   split="""test"""				)

    UpperCamelCase			  =      Image.open(ds[0]["""file"""]				).convert("""RGB"""				)

    UpperCamelCase			  =      image_processing(lowerCamelCase_   ,   return_tensors="""pt"""				)

    self.assertEqual(encoding.pixel_values.shape   ,   (1, 3, 224, 224)				)
    self.assertEqual(len(encoding.words				)   ,   len(encoding.boxes				)				)

    # fmt: off
    # the words and boxes were obtained with Tesseract 4.1.1
    UpperCamelCase			  =      [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
    UpperCamelCase			  =      [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
    # fmt: on

    self.assertListEqual(encoding.words   ,   lowerCamelCase_				)
    self.assertListEqual(encoding.boxes   ,   lowerCamelCase_				)

    # with apply_OCR = False
    UpperCamelCase			  =      LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase_				)

    UpperCamelCase			  =      image_processing(lowerCamelCase_   ,   return_tensors="""pt"""				)

    self.assertEqual(encoding.pixel_values.shape   ,   (1, 3, 224, 224)				)

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     _SCREAMING_SNAKE_CASE 		=      """0.12"""  # assumed parallelism: 8


if is_torch_available():
     import torch





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None       )		->	int:







 '''simple docstring'''
 if rng is None:
  UpperCamelCase			  =      random.Random()

 UpperCamelCase			  =      1
 for dim in shape:
  total_dims *= dim

 UpperCamelCase			  =      []
 for _ in range(UpperCamelCase_       ):
  values.append(rng.randint(0							,					vocab_size - 1       )       )

 UpperCamelCase			  =      np.array(UpperCamelCase_							,					dtype=jnp.intaa       ).reshape(UpperCamelCase_       )

 return output





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=None       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      ids_tensor(UpperCamelCase_							,					vocab_size=2							,					rng=UpperCamelCase_       )
 # make sure that at least one token is attended to for each batch
 UpperCamelCase			  =      1
 return attn_mask
@require_flax
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				()







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()

    # cut to half length & take max batch_size 3
    UpperCamelCase			  =      2
    UpperCamelCase			  =      inputs["""input_ids"""].shape[-1] // 2
    UpperCamelCase			  =      inputs["""input_ids"""][:max_batch_size, :sequence_length]

    UpperCamelCase			  =      jnp.ones_like(lowerCamelCase_				)
    UpperCamelCase			  =      attention_mask[:max_batch_size, :sequence_length]

    # generate max 5 tokens
    UpperCamelCase			  =      input_ids.shape[-1] + 5
    if config.eos_token_id is not None and config.pad_token_id is None:
     # hack to allow generate for models such as GPT2 as is done in `generate()`
     UpperCamelCase			  =      config.eos_token_id
    return config, input_ids, attention_mask, max_length







   @is_pt_flax_cross_test
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
     UpperCamelCase			  =      getattr(lowerCamelCase_   ,   lowerCamelCase_				)
     UpperCamelCase			  =      pt_model_class(lowerCamelCase_				).eval()
     UpperCamelCase			  =      load_flax_weights_in_pytorch_model(lowerCamelCase_   ,   flax_model.params				)

     UpperCamelCase			  =      flax_model.generate(lowerCamelCase_				).sequences
     UpperCamelCase			  =      pt_model.generate(torch.tensor(lowerCamelCase_   ,   dtype=torch.long				)				)

     if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
      UpperCamelCase			  =      flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

     self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,   flax_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[0]   ,   input_ids.shape[0] * config.num_return_sequences				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0.8
    UpperCamelCase			  =      10
    UpperCamelCase			  =      0.3
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      2
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)





@require_flax
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
    UpperCamelCase			  =      FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

    UpperCamelCase			  =      """Hello world"""
    UpperCamelCase			  =      tokenizer(lowerCamelCase_   ,   return_tensors="""np"""				).input_ids

    # typos are quickly detected (the correct argument is `do_sample`)
    with self.assertRaisesRegex(lowerCamelCase_   ,   """do_samples"""				):
     model.generate(lowerCamelCase_   ,   do_samples=lowerCamelCase_				)

    # arbitrary arguments that will not be used anywhere are also not accepted
    with self.assertRaisesRegex(lowerCamelCase_   ,   """foo"""				):
     UpperCamelCase			  =      {"""foo""": """bar"""}
     model.generate(lowerCamelCase_   ,   **lowerCamelCase_				)
